Research on depth vision based mobile robot autonomous navigation in underground coal mine

Underground mobile robot is the main force of coal mine robot. Autonomous navigation is the difficulty and the hotspot task in research. Currently three-dimensional environment database which is necessary for au-tonomous navigation of mobile robots in coal mines has not been fully developed. In particularly,the production of high-resolu- tion,multi-information fused,high-precision maps of underground coal mine is still under investigation. In order to solve the problem of autonomous navigation of mobile robot in underground coal mine,a machine vision system based on depth camera was built,and a navigation method based on depth vision was proposed. The autonomous navigation process have two stages: map creation and autonomous operation. In the stage of map creation,① depth vision data was used for feature extracting and matching. Five depth visual feature extraction and matching algorithms were com- pared and tested in ten groups underground coal mine images. Result shows that the algorithm SURF+SURF+FLANN and GFTT+BRIEF+BF have better performance. ② An Iterative Closest Points Bundle Adjustment model for depth vi- sion based localization and mapping problem of mobile robot in underground coal mine was established. ③ The optimal camera poses and landmarks under current observation were estimated by graph optimization. A laboratory scene origi- nal point cloud map containing key poses was established by using the proposed ICP-BA algorithm. In the stage of au- tonomous operation,① the point cloud map was transformed into an octree data structure Octomap which can be used for mobile robot motion planning. Compared with the original point cloud map,Octomap had adjustable resolution,low system resource occupancy and high indexing efficiency. ② The PNP method of 3 d to 2 d projecting was used for re- al-time online relocation. ③ On these basis,A∗ (A Star) path planning based on graph search was taken as the ini- tial value of trajectory planning,and the customized minimum-energy loss functional ( minimum-snap) was used to solve the quadratic programming problem to generate the trajectory for motion controller. Random navigation map was designed in Matlab development environment,the optimal trajectory planning results of time allocations,positions,ve- locities,accelerations and jerks were generated,which verified the correctness of the proposed motion planning algo- rithm. Through the above theoretical analysis and experimental verification,the effectiveness of the proposed depth vi- sion autonomous navigation method for underground coal mine mobile robot was proved.